Overview

Dataset statistics

Number of variables10
Number of observations3161033
Missing cells12799375
Missing cells (%)40.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory241.2 MiB
Average record size in memory80.0 B

Variable types

DateTime1
Text1
Numeric8

Alerts

new_confirmed is highly overall correlated with new_recovered and 2 other fieldsHigh correlation
new_recovered is highly overall correlated with new_confirmed and 3 other fieldsHigh correlation
new_tested is highly overall correlated with new_confirmed and 3 other fieldsHigh correlation
cumulative_confirmed is highly overall correlated with new_recovered and 4 other fieldsHigh correlation
cumulative_deceased is highly overall correlated with new_recovered and 4 other fieldsHigh correlation
cumulative_recovered is highly overall correlated with new_confirmed and 3 other fieldsHigh correlation
cumulative_tested is highly overall correlated with new_tested and 2 other fieldsHigh correlation
new_deceased has 430896 (13.6%) missing valuesMissing
new_recovered has 2862399 (90.6%) missing valuesMissing
new_tested has 3106251 (98.3%) missing valuesMissing
cumulative_deceased has 427813 (13.5%) missing valuesMissing
cumulative_recovered has 2862399 (90.6%) missing valuesMissing
cumulative_tested has 3106193 (98.3%) missing valuesMissing
new_confirmed is highly skewed (γ1 = 81.26117805)Skewed
new_deceased is highly skewed (γ1 = 72.75302092)Skewed
cumulative_confirmed is highly skewed (γ1 = 23.5538026)Skewed
cumulative_deceased is highly skewed (γ1 = 23.18250227)Skewed
new_confirmed has 978365 (31.0%) zerosZeros
new_deceased has 2315471 (73.3%) zerosZeros
new_recovered has 52863 (1.7%) zerosZeros
cumulative_deceased has 304965 (9.6%) zerosZeros

Reproduction

Analysis started2023-09-07 23:59:58.706976
Analysis finished2023-09-08 00:00:44.212359
Duration45.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

date
Date

Distinct948
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.1 MiB
Minimum2020-01-02 00:00:00
Maximum2022-08-22 00:00:00
2023-09-08T02:00:44.286805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:44.521320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

location_key
Text

Distinct4802
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.1 MiB
2023-09-08T02:00:44.723981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.908282
Min length8

Characters and Unicode

Total characters34481438
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDE_BB_12051
2nd rowDE_BB_12051
3rd rowDE_BB_12051
4th rowDE_BB_12051
5th rowDE_BB_12051
ValueCountFrequency (%)
us_ny_nyc 943
 
< 0.1%
us_ca_06059 937
 
< 0.1%
us_ca_06037 936
 
< 0.1%
us_ca_06085 931
 
< 0.1%
us_ca_06033 930
 
< 0.1%
us_ca_06039 930
 
< 0.1%
us_ca_06041 930
 
< 0.1%
us_ca_06043 930
 
< 0.1%
us_ca_06031 930
 
< 0.1%
us_ca_06029 930
 
< 0.1%
Other values (4792) 3151706
99.7%
2023-09-08T02:00:45.071098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 6322066
18.3%
S 3102731
 
9.0%
0 2997393
 
8.7%
1 2783700
 
8.1%
U 2529278
 
7.3%
3 1697801
 
4.9%
2 1526983
 
4.4%
5 1458055
 
4.2%
7 1278137
 
3.7%
4 1090355
 
3.2%
Other values (27) 9694939
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15489638
44.9%
Uppercase Letter 12669734
36.7%
Connector Punctuation 6322066
18.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 3102731
24.5%
U 2529278
20.0%
T 706886
 
5.6%
E 682911
 
5.4%
N 661695
 
5.2%
A 660501
 
5.2%
C 542505
 
4.3%
I 525844
 
4.2%
M 441754
 
3.5%
D 414080
 
3.3%
Other values (16) 2401549
19.0%
Decimal Number
ValueCountFrequency (%)
0 2997393
19.4%
1 2783700
18.0%
3 1697801
11.0%
2 1526983
9.9%
5 1458055
9.4%
7 1278137
8.3%
4 1090355
 
7.0%
9 1045066
 
6.7%
8 947709
 
6.1%
6 664439
 
4.3%
Connector Punctuation
ValueCountFrequency (%)
_ 6322066
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21811704
63.3%
Latin 12669734
36.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 3102731
24.5%
U 2529278
20.0%
T 706886
 
5.6%
E 682911
 
5.4%
N 661695
 
5.2%
A 660501
 
5.2%
C 542505
 
4.3%
I 525844
 
4.2%
M 441754
 
3.5%
D 414080
 
3.3%
Other values (16) 2401549
19.0%
Common
ValueCountFrequency (%)
_ 6322066
29.0%
0 2997393
13.7%
1 2783700
12.8%
3 1697801
 
7.8%
2 1526983
 
7.0%
5 1458055
 
6.7%
7 1278137
 
5.9%
4 1090355
 
5.0%
9 1045066
 
4.8%
8 947709
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34481438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 6322066
18.3%
S 3102731
 
9.0%
0 2997393
 
8.7%
1 2783700
 
8.1%
U 2529278
 
7.3%
3 1697801
 
4.9%
2 1526983
 
4.4%
5 1458055
 
4.2%
7 1278137
 
3.7%
4 1090355
 
3.2%
Other values (27) 9694939
28.1%

new_confirmed
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5621
Distinct (%)0.2%
Missing3424
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean39.36273
Minimum-126883
Maximum128555
Zeros978365
Zeros (%)31.0%
Negative36494
Negative (%)1.2%
Memory size24.1 MiB
2023-09-08T02:00:45.225702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-126883
5-th percentile0
Q10
median3
Q316
95-th percentile142
Maximum128555
Range255438
Interquartile range (IQR)16

Descriptive statistics

Standard deviation384.0166
Coefficient of variation (CV)9.7558428
Kurtosis35109.095
Mean39.36273
Median Absolute Deviation (MAD)3
Skewness81.261178
Sum1.2429211 × 108
Variance147468.75
MonotonicityNot monotonic
2023-09-08T02:00:45.385015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 978365
31.0%
1 330132
 
10.4%
2 202210
 
6.4%
3 145392
 
4.6%
4 112865
 
3.6%
5 90948
 
2.9%
6 76573
 
2.4%
7 65297
 
2.1%
8 56360
 
1.8%
9 49741
 
1.6%
Other values (5611) 1049726
33.2%
ValueCountFrequency (%)
-126883 1
< 0.1%
-108794 1
< 0.1%
-103096 1
< 0.1%
-92922 1
< 0.1%
-42787 1
< 0.1%
-42633 1
< 0.1%
-18817 1
< 0.1%
-17808 1
< 0.1%
-9767 1
< 0.1%
-6301 1
< 0.1%
ValueCountFrequency (%)
128555 1
< 0.1%
126476 1
< 0.1%
122043 1
< 0.1%
110441 1
< 0.1%
110038 1
< 0.1%
104395 1
< 0.1%
104310 1
< 0.1%
99926 1
< 0.1%
93992 1
< 0.1%
93321 1
< 0.1%

new_deceased
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct340
Distinct (%)< 0.1%
Missing430896
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean0.42219273
Minimum-798
Maximum1258
Zeros2315471
Zeros (%)73.3%
Negative10129
Negative (%)0.3%
Memory size24.1 MiB
2023-09-08T02:00:45.507686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-798
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum1258
Range2056
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6603935
Coefficient of variation (CV)8.6699586
Kurtosis16453.964
Mean0.42219273
Median Absolute Deviation (MAD)0
Skewness72.753021
Sum1152644
Variance13.398481
MonotonicityNot monotonic
2023-09-08T02:00:45.623422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2315471
73.3%
1 226666
 
7.2%
2 74887
 
2.4%
3 34325
 
1.1%
4 18943
 
0.6%
5 12083
 
0.4%
-1 8311
 
0.3%
6 7793
 
0.2%
7 5469
 
0.2%
8 4003
 
0.1%
Other values (330) 22186
 
0.7%
(Missing) 430896
 
13.6%
ValueCountFrequency (%)
-798 1
< 0.1%
-512 1
< 0.1%
-494 1
< 0.1%
-452 1
< 0.1%
-417 1
< 0.1%
-382 1
< 0.1%
-371 1
< 0.1%
-352 1
< 0.1%
-343 1
< 0.1%
-317 1
< 0.1%
ValueCountFrequency (%)
1258 1
< 0.1%
1011 1
< 0.1%
930 1
< 0.1%
829 1
< 0.1%
631 1
< 0.1%
598 1
< 0.1%
578 1
< 0.1%
577 1
< 0.1%
576 1
< 0.1%
573 1
< 0.1%

new_recovered
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1101
Distinct (%)0.4%
Missing2862399
Missing (%)90.6%
Infinite0
Infinite (%)0.0%
Mean30.949051
Minimum-4
Maximum5532
Zeros52863
Zeros (%)1.7%
Negative11
Negative (%)< 0.1%
Memory size24.1 MiB
2023-09-08T02:00:45.732346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile0
Q11
median8
Q330
95-th percentile130
Maximum5532
Range5536
Interquartile range (IQR)29

Descriptive statistics

Standard deviation79.0898
Coefficient of variation (CV)2.5554838
Kurtosis362.117
Mean30.949051
Median Absolute Deviation (MAD)8
Skewness12.611041
Sum9242439
Variance6255.1965
MonotonicityNot monotonic
2023-09-08T02:00:45.851659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52863
 
1.7%
1 29361
 
0.9%
2 18178
 
0.6%
3 13131
 
0.4%
4 10741
 
0.3%
5 8957
 
0.3%
6 7678
 
0.2%
7 6787
 
0.2%
8 6092
 
0.2%
9 5580
 
0.2%
Other values (1091) 139266
 
4.4%
(Missing) 2862399
90.6%
ValueCountFrequency (%)
-4 1
 
< 0.1%
-1 10
 
< 0.1%
0 52863
1.7%
1 29361
0.9%
2 18178
 
0.6%
3 13131
 
0.4%
4 10741
 
0.3%
5 8957
 
0.3%
6 7678
 
0.2%
7 6787
 
0.2%
ValueCountFrequency (%)
5532 1
< 0.1%
4393 1
< 0.1%
3905 1
< 0.1%
3858 1
< 0.1%
3412 1
< 0.1%
3366 1
< 0.1%
3326 1
< 0.1%
3315 1
< 0.1%
3295 1
< 0.1%
3177 1
< 0.1%

new_tested
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10585
Distinct (%)19.3%
Missing3106251
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean3364.3705
Minimum0
Maximum365607
Zeros2348
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size24.1 MiB
2023-09-08T02:00:45.978218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q175.25
median468
Q32257
95-th percentile12022.7
Maximum365607
Range365607
Interquartile range (IQR)2181.75

Descriptive statistics

Standard deviation14135.559
Coefficient of variation (CV)4.2015465
Kurtosis184.84185
Mean3364.3705
Median Absolute Deviation (MAD)458
Skewness12.178943
Sum1.8430695 × 108
Variance1.9981403 × 108
MonotonicityNot monotonic
2023-09-08T02:00:46.098509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2348
 
0.1%
1 676
 
< 0.1%
2 525
 
< 0.1%
3 419
 
< 0.1%
4 410
 
< 0.1%
6 324
 
< 0.1%
5 314
 
< 0.1%
7 308
 
< 0.1%
9 270
 
< 0.1%
8 262
 
< 0.1%
Other values (10575) 48926
 
1.5%
(Missing) 3106251
98.3%
ValueCountFrequency (%)
0 2348
0.1%
1 676
 
< 0.1%
2 525
 
< 0.1%
3 419
 
< 0.1%
4 410
 
< 0.1%
5 314
 
< 0.1%
6 324
 
< 0.1%
7 308
 
< 0.1%
8 262
 
< 0.1%
9 270
 
< 0.1%
ValueCountFrequency (%)
365607 1
< 0.1%
343873 1
< 0.1%
340418 1
< 0.1%
339044 1
< 0.1%
338585 1
< 0.1%
329775 1
< 0.1%
324969 1
< 0.1%
322485 1
< 0.1%
315764 1
< 0.1%
315376 1
< 0.1%

cumulative_confirmed
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct124175
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11028.926
Minimum0
Maximum3200023
Zeros12672
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size24.1 MiB
2023-09-08T02:00:46.220047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q1344
median1753
Q36289
95-th percentile42918
Maximum3200023
Range3200023
Interquartile range (IQR)5945

Descriptive statistics

Standard deviation51730.778
Coefficient of variation (CV)4.6904636
Kurtosis924.22555
Mean11028.926
Median Absolute Deviation (MAD)1674
Skewness23.553803
Sum3.48628 × 1010
Variance2.6760734 × 109
MonotonicityNot monotonic
2023-09-08T02:00:46.342264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 30371
 
1.0%
2 20127
 
0.6%
3 16801
 
0.5%
4 14211
 
0.4%
5 13011
 
0.4%
0 12672
 
0.4%
6 12424
 
0.4%
7 10909
 
0.3%
8 10050
 
0.3%
9 8932
 
0.3%
Other values (124165) 3011525
95.3%
ValueCountFrequency (%)
0 12672
0.4%
1 30371
1.0%
2 20127
0.6%
3 16801
0.5%
4 14211
0.4%
5 13011
0.4%
6 12424
0.4%
7 10909
 
0.3%
8 10050
 
0.3%
9 8932
 
0.3%
ValueCountFrequency (%)
3200023 1
 
< 0.1%
3190808 3
< 0.1%
3177769 4
< 0.1%
3168105 3
< 0.1%
3153757 4
< 0.1%
3140955 3
< 0.1%
3122700 4
< 0.1%
3107063 3
< 0.1%
3085376 4
< 0.1%
3068779 3
< 0.1%

cumulative_deceased
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct9600
Distinct (%)0.4%
Missing427813
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean170.05813
Minimum0
Maximum41437
Zeros304965
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size24.1 MiB
2023-09-08T02:00:46.479403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median36
Q3112
95-th percentile603
Maximum41437
Range41437
Interquartile range (IQR)106

Descriptive statistics

Standard deviation821.99204
Coefficient of variation (CV)4.8335944
Kurtosis751.84258
Mean170.05813
Median Absolute Deviation (MAD)35
Skewness23.182502
Sum4.648063 × 108
Variance675670.92
MonotonicityNot monotonic
2023-09-08T02:00:46.614722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 304965
 
9.6%
1 115135
 
3.6%
2 78871
 
2.5%
3 59260
 
1.9%
4 50601
 
1.6%
5 42186
 
1.3%
6 41392
 
1.3%
7 37128
 
1.2%
8 34948
 
1.1%
9 34548
 
1.1%
Other values (9590) 1934186
61.2%
(Missing) 427813
 
13.5%
ValueCountFrequency (%)
0 304965
9.6%
1 115135
 
3.6%
2 78871
 
2.5%
3 59260
 
1.9%
4 50601
 
1.6%
5 42186
 
1.3%
6 41392
 
1.3%
7 37128
 
1.2%
8 34948
 
1.1%
9 34548
 
1.1%
ValueCountFrequency (%)
41437 1
< 0.1%
41417 1
< 0.1%
35783 2
< 0.1%
35781 1
< 0.1%
35776 1
< 0.1%
35764 1
< 0.1%
35756 1
< 0.1%
35742 1
< 0.1%
35730 1
< 0.1%
35718 1
< 0.1%

cumulative_recovered
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32329
Distinct (%)10.8%
Missing2862399
Missing (%)90.6%
Infinite0
Infinite (%)0.0%
Mean5924.1444
Minimum0
Maximum192757
Zeros10637
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size24.1 MiB
2023-09-08T02:00:46.740569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1266
median2551
Q37612
95-th percentile22327
Maximum192757
Range192757
Interquartile range (IQR)7346

Descriptive statistics

Standard deviation9881.7414
Coefficient of variation (CV)1.6680453
Kurtosis50.742264
Mean5924.1444
Median Absolute Deviation (MAD)2505
Skewness5.0970458
Sum1.769151 × 109
Variance97648812
MonotonicityNot monotonic
2023-09-08T02:00:46.870886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10637
 
0.3%
1 3313
 
0.1%
2 2295
 
0.1%
5 1961
 
0.1%
3 1520
 
< 0.1%
4 1418
 
< 0.1%
8 1318
 
< 0.1%
10 1144
 
< 0.1%
6 1127
 
< 0.1%
13 1099
 
< 0.1%
Other values (32319) 272802
 
8.6%
(Missing) 2862399
90.6%
ValueCountFrequency (%)
0 10637
0.3%
1 3313
 
0.1%
2 2295
 
0.1%
3 1520
 
< 0.1%
4 1418
 
< 0.1%
5 1961
 
0.1%
6 1127
 
< 0.1%
7 1012
 
< 0.1%
8 1318
 
< 0.1%
9 1073
 
< 0.1%
ValueCountFrequency (%)
192757 2
< 0.1%
192752 1
< 0.1%
192750 1
< 0.1%
192727 1
< 0.1%
192692 1
< 0.1%
192657 1
< 0.1%
192630 1
< 0.1%
192572 1
< 0.1%
192559 1
< 0.1%
192548 1
< 0.1%

cumulative_tested
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47544
Distinct (%)86.7%
Missing3106193
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean1251654
Minimum0
Maximum71996051
Zeros948
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.1 MiB
2023-09-08T02:00:47.001862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile86
Q119715
median158276
Q3789612
95-th percentile4682827.6
Maximum71996051
Range71996051
Interquartile range (IQR)769897

Descriptive statistics

Standard deviation4844666.9
Coefficient of variation (CV)3.8706119
Kurtosis122.39716
Mean1251654
Median Absolute Deviation (MAD)156278
Skewness10.215455
Sum6.8640705 × 1010
Variance2.3470797 × 1013
MonotonicityNot monotonic
2023-09-08T02:00:47.143480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 948
 
< 0.1%
1 212
 
< 0.1%
3 163
 
< 0.1%
2 138
 
< 0.1%
4 101
 
< 0.1%
6 77
 
< 0.1%
5 69
 
< 0.1%
7 59
 
< 0.1%
8 55
 
< 0.1%
9 50
 
< 0.1%
Other values (47534) 52968
 
1.7%
(Missing) 3106193
98.3%
ValueCountFrequency (%)
0 948
< 0.1%
1 212
 
< 0.1%
2 138
 
< 0.1%
3 163
 
< 0.1%
4 101
 
< 0.1%
5 69
 
< 0.1%
6 77
 
< 0.1%
7 59
 
< 0.1%
8 55
 
< 0.1%
9 50
 
< 0.1%
ValueCountFrequency (%)
71996051 2
< 0.1%
71995700 1
< 0.1%
71992584 1
< 0.1%
71964228 1
< 0.1%
71952080 1
< 0.1%
71936128 1
< 0.1%
71897794 1
< 0.1%
71851489 1
< 0.1%
71803926 1
< 0.1%
71753607 1
< 0.1%

Interactions

2023-09-08T02:00:34.468422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:20.156974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:23.035865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:25.435064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:26.740039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:28.515760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:31.254747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:33.410822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:34.623647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:20.693571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:23.518912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:25.622658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:26.893743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:29.098059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:31.777957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:33.585610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:34.777718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:20.866590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:23.672811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:25.781056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:27.047490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:29.280152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:31.942509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:33.739885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:34.901815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:21.009614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:23.804606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:25.900228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:27.161387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:29.418377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:32.065408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:33.849519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:35.058527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:21.667568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:24.332213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:26.079641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:27.319158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:29.927062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:32.552459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:33.969886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:35.211742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:22.177530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:24.874497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:26.271028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:27.468325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:30.424914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:32.995530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:34.111779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:35.351980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:22.341762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:25.055702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:26.457533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:27.764493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:30.601375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:33.126049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:34.242175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:35.499256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:22.488231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:25.216755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:26.605607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:27.874673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:30.758580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:33.254075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T02:00:34.365861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-08T02:00:47.248604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
new_confirmednew_deceasednew_recoverednew_testedcumulative_confirmedcumulative_deceasedcumulative_recoveredcumulative_tested
new_confirmed1.0000.4120.9050.6310.4720.4350.7590.463
new_deceased0.4121.0000.4590.4290.2940.3150.2620.335
new_recovered0.9050.4591.000NaN0.6930.6760.697NaN
new_tested0.6310.429NaN1.0000.8380.842NaN0.867
cumulative_confirmed0.4720.2940.6930.8381.0000.9541.0000.984
cumulative_deceased0.4350.3150.6760.8420.9541.0000.9590.963
cumulative_recovered0.7590.2620.697NaN1.0000.9591.000NaN
cumulative_tested0.4630.335NaN0.8670.9840.963NaN1.000

Missing values

2023-09-08T02:00:35.897290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-08T02:00:37.898276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-08T02:00:41.917516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datelocation_keynew_confirmednew_deceasednew_recoverednew_testedcumulative_confirmedcumulative_deceasedcumulative_recoveredcumulative_tested
02020-03-15DE_BB_120512.00.02.0NaN2.00.02.0NaN
12020-03-17DE_BB_120511.00.01.0NaN3.00.03.0NaN
22020-03-19DE_BB_120512.00.02.0NaN5.00.05.0NaN
32020-03-20DE_BB_120511.00.01.0NaN6.00.06.0NaN
42020-03-22DE_BB_120512.00.02.0NaN8.00.08.0NaN
52020-03-23DE_BB_120513.00.03.0NaN11.00.011.0NaN
62020-03-24DE_BB_120511.00.01.0NaN12.00.012.0NaN
72020-03-26DE_BB_120512.00.02.0NaN14.00.014.0NaN
82020-03-28DE_BB_120511.00.01.0NaN15.00.015.0NaN
92020-03-29DE_BB_120511.00.01.0NaN16.00.016.0NaN
datelocation_keynew_confirmednew_deceasednew_recoverednew_testedcumulative_confirmedcumulative_deceasedcumulative_recoveredcumulative_tested
31610232022-05-04US_WY_560450.00.0NaNNaN1589.018.0NaNNaN
31610242022-05-05US_WY_560450.00.0NaNNaN1589.018.0NaNNaN
31610252022-05-06US_WY_560450.00.0NaNNaN1589.018.0NaNNaN
31610262022-05-07US_WY_560450.00.0NaNNaN1589.018.0NaNNaN
31610272022-05-08US_WY_560450.00.0NaNNaN1589.018.0NaNNaN
31610282022-05-09US_WY_560450.00.0NaNNaN1589.018.0NaNNaN
31610292022-05-10US_WY_56045-1.00.0NaNNaN1588.018.0NaNNaN
31610302022-05-11US_WY_560450.00.0NaNNaN1588.018.0NaNNaN
31610312022-05-12US_WY_560450.00.0NaNNaN1588.018.0NaNNaN
31610322022-05-13US_WY_560450.00.0NaNNaN1588.018.0NaNNaN